Matching incoming event notifications against received subscriptions is a fundamental part of every publish-subscribe infrastructure. In the case of content-based systems this is a fairly complex and time consuming task, whose performance impacts that of the entire system. In the past, several algorithms have been proposed for efficient content-based event matching. While they differ in most aspects, they have in common the fact of being conceived to run on conventional, sequential hardware. On

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... the other hand, modern Graphical Processing Units (GPUs) offer off-the-shelf, highly parallel hardware, at a reasonable cost. Unfortunately, GPUs introduce a totally new model of computation, which requires algorithms to be fully re-designed. In this paper, we describe a new content-based matching algorithm designed to run efficiently on CUDA, a widespread architecture for general purpose programming on GPUs. A detailed comparison with SFF, the matching algorithm of Siena, known for its efficiency, demonstrates how the use of GPUs can bring impressive speedups in content-based matching. At the same time, this analysis demonstrates the peculiar aspects of CUDA programming that mostly impact performance.